Glasgow coma scale pupil score (GCS-P) and the hospital mortality in severe traumatic brain injury: analysis of 1,066 Brazilian patients

Traumatic brain injury in elderly population: A global systematic review and meta-analysis of in-hospital mortality and risk factors among 2.22 million individuals

BACKGROUND

Traumatic brain injury (TBI) among elderly individuals poses a significant global health concern due to the increasing ageing population.

METHODS

We searched PubMed, Cochrane Library, and Embase from database inception to Feb 1, 2024. Studies performed in inpatient settings reporting in-hospital mortality of elderly people (≥60 years) with TBI and/or identifying risk factors predictive of such outcomes, were included. Data were extracted from published reports, in-hospital mortality as our main outcome was synthesized in the form of rates, and risk factors predicting in-hospital mortality was synthesized in the form of odds ratios. Subgroup analyses, meta-regression and dose-response meta-analysis were used in our analyses.

FINDINGS

We included 105 studies covering 2217,964 patients from 30 countries/regions. The overall in-hospital mortality of elderly patients with TBI was 16 % (95 % CI 15 %-17 %) from 70 studies. In-hospital mortality was 5 % (95 % CI, 3 %-7 %), 18 % (95 % CI, 12 %-24 %), 65 % (95 % CI, 59 %-70 %) for mild, moderate and severe subgroups from 10, 7, and 23 studies, respectively. A decrease in in-hospital mortality over years was observed in overall (1981-2022) and in severe (1986-2022) elderly patients with TBI. Older age 1.69 (95 % CI, 1.58-1.82, P < 0.001), male gender 1.34 (95 % CI, 1.25-1.42, P < 0.001), clinical conditions including traffic-related cause of injury 1.22 (95 % CI, 1.02-1.45, P = 0.029), GCS moderate (GCS 9-12 compared to GCS 13-15) 4.33 (95 % CI, 3.13-5.99, P < 0.001), GCS severe (GCS 3-8 compared to GCS 13-15) 23.09 (95 % CI, 13.80-38.63, P < 0.001), abnormal pupillary light reflex 3.22 (95 % CI, 2.09-4.96, P < 0.001), hypotension after injury 2.88 (95 % CI, 1.06-7.81, P = 0.038), polytrauma 2.31 (95 % CI, 2.03-2.62, P < 0.001), surgical intervention 2.21 (95 % CI, 1.22-4.01, P = 0.009), pre-injury health conditions including pre-injury comorbidity 1.52 (95 % CI, 1.24-1.86, P = 0.0020), and pre-injury anti-thrombotic therapy 1.51 (95 % CI, 1.23-1.84, P < 0.001) were related to higher in-hospital mortality in elderly patients with TBI. Subgroup analyses according to multiple types of anti-thrombotic drugs with at least two included studies showed that anticoagulant therapy 1.70 (95 % CI, 1.04-2.76, P = 0.032), Warfarin 2.26 (95 % CI, 2.05-2.51, P < 0.001), DOACs 1.99 (95 % CI, 1.43-2.76, P < 0.001) were related to elevated mortality. Dose-response meta-analysis of age found an odds ratio of 1.029 (95 % CI, 1.024-1.034, P < 0.001) for every 1-year increase in age on in-hospital mortality.

CONCLUSIONS

In the field of elderly patients with TBI, the overall in-hospital mortality and its temporal-spatial feature, the subgroup in-hospital mortalities according to injury severity, and dose-response meta-analysis of age were firstly comprehensively summarized. Substantial key risk factors, including the ones previously not elucidated, were identified. Our study is thus of help in underlining the importance of treating elderly TBI, providing useful information for healthcare providers, and initiating future management guidelines. This work underscores the necessity of integrating elderly TBI treatment and management into broader health strategies to address the challenges posed by the aging global population.

REVIEW REGISTRATION

PROSPERO CRD42022323231.

A Meta-analysis of Predicting Disorders of Consciousness After Traumatic Brain Injury by Machine Learning Models

Objective

This study pursued a meta-analysis to evaluate the predictive accuracy of machine learning (ML) models in determining disorders of consciousness (DOC) among patients with traumatic brain injury (TBI).

Methods

A comprehensive literature search was conducted to identify ML applications in the establishment of a predictive model of DOC after TBI as of August 6, 2023. Two independent reviewers assessed publication eligibility based on predefined criteria. The predictive accuracy was measured using areas under the receiver operating characteristic curves (AUCs). Subsequently, a random-effects model was employed to estimate the overall effect size, and statistical heterogeneity was determined based on I2 statistic. Additionally, funnel plot asymmetry was employed to examine publication bias. Finally, subgroup analyses were performed based on age, ML type, and relevant clinical outcomes.

Results

Final analyses incorporated a total of 46 studies. Both the overall and subgroup analyses exhibited considerable statistical heterogeneity. Machine learning predictions for DOC in TBI yielded an overall pooled AUC of 0.83 (95% CI: 0.82-0.84). Subgroup analysis based on age revealed that the ML model in pediatric patients yielded an overall combined AUC of 0.88 (95% CI: 0.80-0.95); among the model subgroups, logistic regression was the most frequently employed, with an overall pooled AUC of 0.85 (95% CI: 0.83-0.87). In the clinical outcome subgroup analysis, the overall pooled AUC for distinguishing between consciousness recovery and consciousness disorders was 0.84 (95% CI: 0.82-0.85).

Conclusion

The findings of this meta-analysis demonstrated outstanding accuracy of ML models in predicting DOC among patients with brain injuries, which presented substantial research value and potential of ML application in this domain.

Effect of low fibrinogen level on in-hospital mortality and 6-month functional outcome of TBI patients, a single center experience

In patients affected by traumatic brain injury (TBI), hypofibrinogenemia within the initial hours of trauma can be expected due to vascular and inflammatory changes. In this study, we aimed to evaluate the effect of hypofibrinogenemia on the in-hospital mortality and 6-month functional outcomes of TBI patients, admitted to Rajaee Hospital, a referral trauma center in Shiraz, Iran. This study included all TBI patients admitted to our center who had no prior history of coagulopathy or any systemic disease, were alive on arrival, and had not received any blood product before admission. On admission, hospitalization, imaging, and 6-month follow-up information of included patients were extracted from the TBI registry database. The baseline characteristics of patients with fibrinogen levels of less than 150 mg/dL were compared with the cases with higher levels. To assess the effect of low fibrinogen levels on in-hospital mortality, a uni- and multivariate was conducted between those who died in hospital and survivors. Based on the 6-month GOSE score of patients, those with GOSE < 4 (unfavorable outcome) were compared with those with a favorable outcome. A total of 3049 patients (84.3% male, 15.7% female), with a mean age of 39.25 ± 18.87, met the eligibility criteria of this study. 494 patients had fibrinogen levels < 150 mg/dl, who were mostly younger and had lower average GCS scores in comparison to cases with higher fibrinogen levels. By comparison of the patients who died during hospitalization and survivors, it was shown that fibrinogen < 150 mg/dl is among the prognostic factors for in-hospital mortality (OR:1.75, CI: 1.32:2.34, P-value < 0.001), while the comparison between patients with the favorable and unfavorable functional outcome at 6-month follow-up, was not in favor of prognostic effect of low fibrinogen level (OR: 0.80, CI: 0.58: 1.11, P-value: 0.19). Hypofibrinogenemia is associated with in-hospital mortality of TBI patients, along with known factors such as higher age and lower initial GCS score. However, it is not among the prognostic factors of midterm functional outcome.